Lead Data Scientist- IBM Watson

Fusemachines•New York City, NY

About The Position

A Lead Data Scientist is responsible for designing and implementing data-driven solutions to complex business problems. The role requires extensive experience in data analysis, agentic AI, statistical modeling, machine learning, and data visualization, as well as the ability to lead a team of data scientists and collaborate with cross-functional teams.

Requirements

Education: Bachelor's, Master's, PhD, or advanced training in applied mathematics, engineering, computer science, or a similar related field.
Experience: 6+ years of total experience in Data Science, Machine Learning & Generative AI.
Cloud Computing: 4+ years of hands-on experience with AWS, including deep expertise in deploying models and managing compute environments.
IBM WatsonX: 2+ years of hands-on experience with IBM WatsonX
Agentic AI Tools: Experience with LangGraph, Google ADK or similar.
Agentic Architectures : Experience with advanced RAG & multi-agent systems.
Programming: Strong programming skills in languages such as Python, R, C++, and SQL.
Frameworks: Hands-on experience with ML frameworks, such as PyTorch or TensorFlow.
Leadership: Experience leading data science teams and managing multiple projects simultaneously.
Soft Skills: Strong problem-solving skills, attention to detail, and excellent communication skills (both written and verbal).

Responsibilities

Team Leadership: Lead a team of data scientists to develop innovative solutions to complex business problems. Mentor and develop the skills of junior data scientists and provide feedback and guidance to help them improve their work.
Collaboration: Collaborate with cross-functional teams, including business stakeholders, product managers, software engineers, and data engineers to develop and implement data-driven solutions.
Strategic Assessment: Assess the business needs of clients and identify areas where AI can be used to improve processes, reduce costs, or increase revenue.
Solution Design: Design and implement statistical models, machine learning algorithms, predictive analytics models, and agentic systems to solve business problems.
Communication: Communicate technical insights and recommendations to non-technical stakeholders in a clear and concise manner.
Continuous Learning: Stay up-to-date with the latest developments in data science, machine learning, and artificial intelligence, and apply new technologies and techniques to solve business problems.
Pipeline Management: Responsible for developing, implementing, and managing end-to-end machine learning pipelines. This will involve building, deploying, and maintaining machine learning models, as well as ensuring data quality and system stability.

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